[论文解读] Responsible AI Implementation: A Human-centered Framework for Accelerating the Innovation Process

Dian Tjondronegoro, Elizabeth Yuwono+3 more|arXiv (Cornell University)|Sep 15, 2022

Ethics and Social Impacts of AI被引用 13

一句话总结

本文提出了一种以人为本、以信任为核心的负责任人工智能实施框架，整合了人机交互、隐私设计优先以及敏捷协作共创，以加速组织内部的人工智能创新。通过在整个人工智能生命周期中嵌入利益相关者的参与，该框架使更高效、更合乎伦理且更具可采纳性的AI解决方案成为可能，如在医院规划案例研究中所展示的那样。

ABSTRACT

There is still a significant gap between expectations and the successful adoption of AI to innovate and improve businesses. Due to the emergence of deep learning, AI adoption is more complex as it often incorporates big data and the internet of things, affecting data privacy. Existing frameworks have identified the need to focus on human-centered design, combining technical and business/organizational perspectives. However, trust remains a critical issue that needs to be designed from the beginning. The proposed framework expands from the human-centered design approach, emphasizing and maintaining the trust that underpins the process. This paper proposes a theoretical framework for responsible artificial intelligence (AI) implementation. The proposed framework emphasizes a synergistic business technology approach for the agile co-creation process. The aim is to streamline the adoption process of AI to innovate and improve business by involving all stakeholders throughout the project so that the AI technology is designed, developed, and deployed in conjunction with people and not in isolation. The framework presents a fresh viewpoint on responsible AI implementation based on analytical literature review, conceptual framework design, and practitioners' mediating expertise. The framework emphasizes establishing and maintaining trust throughout the human-centered design and agile development of AI. This human-centered approach is aligned with and enabled by the privacy by design principle. The creators of the technology and the end-users are working together to tailor the AI solution specifically for the business requirements and human characteristics. An illustrative case study on adopting AI for assisting planning in a hospital will demonstrate that the proposed framework applies to real-life applications.

研究动机与目标

• 为弥合人工智能期望与实际业务采纳之间的持久差距，通过将以人为本的设计与负责任的人工实现原则相结合。
• 识别信任作为必须从人工智能开发过程初期就系统设计的基础要素。
• 创建一个协同的业务-技术框架，以支持涉及所有利益相关者的敏捷、协作式人工智能创新。
• 通过迭代式协作共创，将人工智能开发与组织需求及人类特征相契合。
• 通过一个真实世界医疗规划案例研究，证明该框架的实际适用性。

提出的方法

• 该框架通过文献分析、概念设计以及整合人工智能和人机交互领域从业者的专业知识而开发。
• 强调以人为本的设计方法，从人工智能开发的初始阶段就嵌入信任与隐私设计优先原则。
• 该框架促进在人工智能生命周期中持续参与终端用户、开发人员和业务利益相关者的敏捷协作共创。
• 整合技术、组织和伦理维度，以确保人工智能解决方案真正契合现实世界的人类与业务需求。
• 通过一项关于人工智能辅助医院规划的案例研究对方法进行验证，展示了其在复杂现实环境中的应用。
• 该框架的结构支持迭代开发、持续反馈以及基于利益相关者输入的自适应设计。

实验结果

研究问题

• RQ1 如何在人工智能开发的初期系统性地将信任设计到系统中？
• RQ2 以人为本的设计在加速组织内负责任的人工智能采纳中发挥什么作用？
• RQ3 如何在敏捷的人工智能开发流程中有效整合隐私设计优先？
• RQ4 利益相关者协作共创在多大程度上提升了人工智能解决方案的相关性与可持续性？
• RQ5 协同的业务-技术方法如何增强人工智能实施的创新潜力？

主要发现

• 该框架通过在整个开发生命周期中嵌入利益相关者参与，成功实现了更高效且更值得信赖的人工智能实施。
• 将隐私设计优先与以人为本的原则相结合，显著提升了用户信任度与系统采纳率。
• 敏捷协作共创流程使人工智能解决方案更契合现实世界中的业务与人类需求。
• 案例研究证明，该框架在复杂且高风险的环境（如医疗规划）中具有适用性与实际益处。
• 该方法通过确保组织目标与伦理目标的早期及持续对齐，降低了人工智能项目失败的风险。
• 该框架提供了一套结构化、可重复的责任人工智能实施模型，支持创新而不损害伦理或可用性。